# Regression vs Correlation Analysis

Last Updated on March 19, 2022 by QCity Editorial Stuff

Regression analysis is a statistical technique that one can use to determine the relationship between two or more variables. The data points are plotted on an X-Y scatterplot, and then a linear regression line is calculated. Correlation analysis, on the other hand, assesses how much two variables change together about each other. To calculate correlation, we need to know the Pearson correlation coefficient formula_1 which takes into account both the covariance and variance of both variables in question. The sign of this formula will tell us if there’s a positive (same direction), negative (opposite direction), or no correlation at all (zero).

## What is Regression analysis?

Regression analysis is a statistical technique used to find relationships between two variables. It can be used for many different types of data, including sales and marketing information. The regression equation may look like this: y=a+bx where y is the dependent variable, x is the independent variable, and a and b are constants that must be calculated. The slope of line (b) indicates how much an increase in one variable affects another variable on average; or how strong their relationship is.

Regression analysis is a statistical technique used to identify the relationship between variables by estimating parameters and testing hypotheses about the relationships. Regression analysis can be applied to both qualitative and quantitative data. The linear regression model, for example, assumes that there is a linear association between two numerical variables such as weight and height in humans or time spent watching TV and hours of sleep in teenagers.

Regression analysis involves determining which variable should be the criterion (dependent) variable while another variable will act as an explanatory (independent) variable. For instance, if we wanted to know what factors influence how much time people spend watching TV, then one might analyze their age, income level, education level, etc., with hours of TV watched per day being our dependent measure.

## What is Correlation analysis?

Correlation analysis is a statistical technique that is used to identify relationships between two or more variables. It can be used to determine the strength and direction of the relationship, as well as to assess the significance of the correlation. In business, correlation analysis can be used to help identify factors that are affecting performance and to develop strategies for improving outcomes.

Correlation analysis is a technique used to reveal the relationship between two variables. The correlation coefficient ranges from -1 to 1, where values close to 1 indicate a strong positive linear association and values close to 0 indicate a lack of linear association. In regression, you can use this technique as one of your models for predicting which variable is going up or down based on what it predicts about the other variable. For example, in this dataset, we have data on several people who smoke cigarettes per day and their average amount of money spent on cigarettes per week: *People who spend more money on cigarettes tend to smoke fewer cigarettes each day.* When you run a correlation analysis, it will tell us that there is an almost perfect negative correlation (.99).

## 10 Differences Between Regression and Correlation analysis

1. Regression analysis is used to determine the relationship between two variables, while correlation analysis is used to test the strength of a linear association between two variables.

2. Correlation does not imply causation, but regression does.

3. A scatterplot can be used for both types of analyses and will show if there’s a pattern in the data.

4. A regression line has an equation that can be derived from it and correlation does not have an equation.

5. The slope of a regression line represents how one variable changes when another variable changes by 1 unit.

6. Correlation coefficients range from -1 to +1 with 0 meaning no linear association at all.

7. Correlation analysis is a statistical technique that measures how well one variable can predict another.

8. A correlation coefficient of 1 indicates a perfect positive linear relationship, while -1 indicates a perfect negative linear relationship.

9. The strength of the correlation between two variables depends on the magnitude and direction of their association.

10. In regression analysis, when there is more than one predictor variable, each variable in the equation affects the outcome variable.

## Interesting Statistics or Facts of  Regression analysis

1. Regression analysis is a statistical technique that can be used to predict the future.

2. The regression model is based on the assumption that there is a linear relationship between two variables.

3. There are many different types of regression models, including multiple regressions and logistic regressions.

4. A common application of regression analysis in business is forecasting sales or demand for products.

5. Regression analyses can also be used to examine data and make predictions about things like stock prices, election results, and economic trends.

6. Linear regression uses least-squares estimation which minimizes the sum of squared errors (SSE) when predicting values from a set of given data points.

## Interesting Statistics or Facts of Correlation analysis

1. The average person has 1,500 dreams per year.

2. One in every four American adults is obese.

3. There are more than 10 million people with Alzheimer’s disease in the United States alone.

4. Oxytocin, a hormone that makes you feel happy and loving after an orgasm or when you’re having a hug from your loved one, can also be released by petting dogs.

5. People who eat breakfast tend to be thinner than those who skip it.

6. A study found that children born to older parents have lower IQs on average than children born to younger parents.

## Conclusion

A basic understanding of the difference between regression and correlation analysis can be a powerful tool for data analytics. Regression is used to find correlations among variables; it helps us determine which factors are related or not, as well as how they relate. Correlation looks at the strength of those relationships but does not establish causation (which can make it less reliable). The definitions may sound complicated on paper, but by breaking them down into simpler terms we can see that these two statistical techniques provide different information about our data sets—and both have their place in business intelligence.

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